Think of AI implementation as investing in an expensive, high-performance oven for your restaurant.
As cool as it may be, an oven is basically just a tool. To offer great cuisine, you still need good-quality products, fantastic chefs, and a list of recipes to cook properly.
The same goes for AI, which should be combined with three additional elements in order to boost your company’s performance and achieve a satisfying return on investment (ROI):
- Data: the products that your AI system will “cook” and transform into precious insights.
- Experienced professionals: a team of “chefs” with the necessary know-how to get the best from your AI solution.
- The right strategy: once you have proper data and an exceptional team, you still need to know how and when to use AI in the right way. That’s your recipe!
The ROI of AI: potential and requirements
Nowadays, there is no industry that gives up on the valuable contribution of artificial intelligence.
From energy to the mining sector, from education to human resources, AI permeates virtually any business, generally ensuring a positive ROI from its implementation.
The return on investment in the AI field can come in four different ways:
- Increased revenues, for example with product recommendation in digital stores
- Cost reduction and improved efficiency by automating business processes
- Risk management, which means using AI for cybersecurity or personnel safety
- Non-Financial factors, such as customer care’s quality and speed improvements
The best sectors in terms of ROI
According to research by ESI ThoughtLab and Deloitte, the top areas in terms of returns from AI investments include customer care (74%), IT operations and infrastructures (69%), planning and decision-making (66%).
While such data is promising, many companies are still unable to achieve satisfactory ROI from their AI projects.
That’s because some basic requirements are needed to get positive results.
“The top areas in terms of returns from AI investments include customer care (74%), IT operations and infrastructures (69%), planning and decision-making (66%).”
Which companies can succeed?
Based on the ESI ThoughtLab survey, all companies achieving high ROI (over 5%) had implemented key practices in data management, results tracking, and security.
Another factor to consider is the companies’ experience and maturity: leading businesses can boast an average of a 4.3% ROI and a relatively fast paycheck period (1.2 years) for their AI projects, compared to the 0.2% ROI and a longer payback period (1.6 years) of beginners.
To sum it up, what companies investing in artificial intelligence should understand first is that AI implementation can only be leveraged in a profitable way with proper preparation, consisting of good data, strategies, and professionals, as we mentioned in our introduction.
Regarding this, let’s delve a little bit more!
1. Achieving ROI with good data
AI is an incredible tool, which is literally reforging our way of living. That’s especially true when we consider its most recent and powerful branches, namely machine learning and deep learning.
The potential of ML lies in its ability to autonomously recognize the key relationships between raw data and create forecast models.
Such superpower can be applied basically anywhere: from marketing, in which ML-based systems scan customer data to personalize ads, to power utilities, which use AI to predict the energy load based on weather conditions.
AI can be really demanding
The fact of being so data-hungry is also AI’s biggest limitation when it comes to leveraging it.
AI and ML cannot generate useful insights from any information you feed them with. Data must be suitable in terms of both quantity and quality.
Regarding the quantitative aspect, there are several tricks to increase our information menu. Our technical manager Konstantin talked about it in this article.
What we need is high-quality data
Moving on to the quality of the data, the situation becomes much more tricky. Our dataset should always be broadly representative and multifaceted, taking into account the numerous conditions under which our ML model can be applied.
This means, for example, that if we are preparing a set containing the information of our customers, it is necessary to consider numerous aspects: personal data, client preferences, potential churn and retention rates, willingness to buy, and so on.
Once this information is gathered, any company that deploys AI can use it to guide each customer interaction.
All of this is easier said than done, because collecting the right data takes time and money, and the risk of compromising or corrupting our dataset with incorrect, biased, or partial information is always around the corner.
A pair of examples
This is particularly evident in speech analysis and the tools that rely on it, such as chatbots. If you train them with only formal texts (e.g. newspaper articles and official documentation) they probably won’t be able to understand the typically informal language of standard users.
And what about systems based on artificial intelligence for the maintenance of industrial machines?
If data collected is not representative of the plant’s operations (eg: sensors keep on running when the machine is off), the AI will have some trouble recognizing between standard and non-standard behaviors and, consequently, understanding if something is going wrong.